Compressed Sensing Using K-SVD Based On Brillouin Optical Time Domain Analyzer

Brillouin optical time domain analysis(BOTDA)is widely used in various largescale infrastructure health monitoring because it can accurately measure temperature and strain,high spatial resolution,long sensing distance and can be measured in harsh environment.Aiming at the problem of long measurement time and large amount of sensing data,this paper proposes a compressed sensing algorithm based on K-means singular value decomposition(K-SVD)to extract the information of BOTDA,and realizes rapid measurement and demodulation without changing any hardware.The specific contents are as follows:(1)A compressed sensing scheme based on K-SVD dictionary learning algorithm as sparse representation is proposed.According to the characteristics of Brillouin gain spectrum(BGS),the sparsity is improved,and the original signal is recovered by using15% sampling points.The number of sampling points required is reduced by 15% and45% compared with principal component analysis(PCA)and discrete cosine transform(DCT)as sparse transform.The measurement speed is improved.(2)Aiming at the problems that the proposed algorithm is difficult to be applied in practice,poor performance in the case of low SNR and slow demodulation speed,a compressed sensing algorithm optimization based on sparse random matrix and regularized orthogonal matching pursuit(ROMP)is proposed,and the algorithm is further optimized by combining different denoising algorithms and similarity measurement.Experiments show that the improved algorithm can reduce the amount of data by up to85% at 1MHz step,and the acquisition time is also shortened to 15% of the traditional acquisition scheme,which reduces the pressure on the acquisition end and data storage equipment,and further realizes the rapid acquisition of Brillouin sensing signal.The demodulation time is also shortened by about 1/4 compared with the above algorithm,and the fast demodulation of Brillouin sensing signal is further realized.To sum up,the compressed sensing algorithm based on K-SVD as sparse transform proposed in this paper can use less data to reconstruct the original signal compared with the previously proposed algorithm.Without changing the hardware,the algorithm has obvious advantages in fast measurement and fast demodulation,and can also be applied to other Brillouin distributed sensing technologies.